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1. Second international consensus report on gaps and opportunities for the clinical translation of precision diabetes medicine

Author

Tobias, Deirdre K, Merino, Jordi, Ahmad, Abrar, Aiken, Catherine, Benham, Jamie L, Bodhini, Dhanasekaran, Clark, Amy L, Colclough, Kevin, Corcoy, Rosa, Cromer, Sara J, Duan, Daisy, Felton, Jamie L, Francis, Ellen C, Gillard, Pieter, Gingras, Véronique, Gaillard, Romy, Haider, Eram, Hughes, Alice, Ikle, Jennifer M, Jacobsen, Laura M, Kahkoska, Anna R, Kettunen, Jarno LT, Kreienkamp, Raymond J, Lim, Lee-Ling, Männistö, Jonna ME, Massey, Robert, Mclennan, Niamh-Maire, Miller, Rachel G, Morieri, Mario Luca, Most, Jasper, Naylor, Rochelle N, Ozkan, Bige, Patel, Kashyap Amratlal, Pilla, Scott J, Prystupa, Katsiaryna, Raghavan, Sridharan, Rooney, Mary R, Schön, Martin, Semnani-Azad, Zhila, Sevilla-Gonzalez, Magdalena, Svalastoga, Pernille, Takele, Wubet Worku, Tam, Claudia Ha-ting, Thuesen, Anne Cathrine B, Tosur, Mustafa, Wallace, Amelia S, Wang, Caroline C, Wong, Jessie J, Yamamoto, Jennifer M, Young, Katherine, Amouyal, Chloé, Andersen, Mette K, Bonham, Maxine P, Chen, Mingling, Cheng, Feifei, Chikowore, Tinashe, Chivers, Sian C, Clemmensen, Christoffer, Dabelea, Dana, Dawed, Adem Y, Deutsch, Aaron J, Dickens, Laura T, DiMeglio, Linda A, Dudenhöffer-Pfeifer, Monika, Evans-Molina, Carmella, Fernández-Balsells, María Mercè, Fitipaldi, Hugo, Fitzpatrick, Stephanie L, Gitelman, Stephen E, Goodarzi, Mark O, Grieger, Jessica A, Guasch-Ferré, Marta, Habibi, Nahal, Hansen, Torben, Huang, Chuiguo, Harris-Kawano, Arianna, Ismail, Heba M, Hoag, Benjamin, Johnson, Randi K, Jones, Angus G, Koivula, Robert W, Leong, Aaron, Leung, Gloria KW, Libman, Ingrid M, Liu, Kai, Long, S Alice, Lowe, William L, Morton, Robert W, Motala, Ayesha A, Onengut-Gumuscu, Suna, Pankow, James S, Pathirana, Maleesa, Pazmino, Sofia, Perez, Dianna, Petrie, John R, Powe, Camille E, Quinteros, Alejandra, Jain, Rashmi, Ray, Debashree, and Ried-Larsen, Mathias

Subjects
Health Services and Systems, Biomedical and Clinical Sciences, Health Sciences, Health Services, Diabetes, Clinical Research, Pediatric, Metabolic and endocrine, Good Health and Well Being, Humans, Precision Medicine, Consensus, Diabetes Mellitus, Evidence-Based Medicine, Medical and Health Sciences, Immunology, Biomedical and clinical sciences, Health sciences
Abstract

Precision medicine is part of the logical evolution of contemporary evidence-based medicine that seeks to reduce errors and optimize outcomes when making medical decisions and health recommendations. Diabetes affects hundreds of millions of people worldwide, many of whom will develop life-threatening complications and die prematurely. Precision medicine can potentially address this enormous problem by accounting for heterogeneity in the etiology, clinical presentation and pathogenesis of common forms of diabetes and risks of complications. This second international consensus report on precision diabetes medicine summarizes the findings from a systematic evidence review across the key pillars of precision medicine (prevention, diagnosis, treatment, prognosis) in four recognized forms of diabetes (monogenic, gestational, type 1, type 2). These reviews address key questions about the translation of precision medicine research into practice. Although not complete, owing to the vast literature on this topic, they revealed opportunities for the immediate or near-term clinical implementation of precision diabetes medicine; furthermore, we expose important gaps in knowledge, focusing on the need to obtain new clinically relevant evidence. Gaps include the need for common standards for clinical readiness, including consideration of cost-effectiveness, health equity, predictive accuracy, liability and accessibility. Key milestones are outlined for the broad clinical implementation of precision diabetes medicine.

Published
2023

2. Investigating Gene-Diet Interactions Impacting the Association Between Macronutrient Intake and Glycemic Traits.

Author

Westerman, Kenneth E, Walker, Maura E, Gaynor, Sheila M, Wessel, Jennifer, DiCorpo, Daniel, Ma, Jiantao, Alonso, Alvaro, Aslibekyan, Stella, Baldridge, Abigail S, Bertoni, Alain G, Biggs, Mary L, Brody, Jennifer A, Chen, Yii-Der Ida, Dupuis, Joseé, Goodarzi, Mark O, Guo, Xiuqing, Hasbani, Natalie R, Heath, Adam, Hidalgo, Bertha, Irvin, Marguerite R, Johnson, W Craig, Kalyani, Rita R, Lange, Leslie, Lemaitre, Rozenn N, Liu, Ching-Ti, Liu, Simin, Moon, Jee-Young, Nassir, Rami, Pankow, James S, Pettinger, Mary, Raffield, Laura M, Rasmussen-Torvik, Laura J, Selvin, Elizabeth, Senn, Mackenzie K, Shadyab, Aladdin H, Smith, Albert V, Smith, Nicholas L, Steffen, Lyn, Talegakwar, Sameera, Taylor, Kent D, de Vries, Paul S, Wilson, James G, Wood, Alexis C, Yanek, Lisa R, Yao, Jie, Zheng, Yinan, Boerwinkle, Eric, Morrison, Alanna C, Fornage, Miriam, Russell, Tracy P, Psaty, Bruce M, Levy, Daniel, Heard-Costa, Nancy L, Ramachandran, Vasan S, Mathias, Rasika A, Arnett, Donna K, Kaplan, Robert, North, Kari E, Correa, Adolfo, Carson, April, Rotter, Jerome I, Rich, Stephen S, Manson, JoAnn E, Reiner, Alexander P, Kooperberg, Charles, Florez, Jose C, Meigs, James B, Merino, Jordi, Tobias, Deirdre K, Chen, Han, and Manning, Alisa K

Subjects
Biomedical and Clinical Sciences, Nutrition and Dietetics, Nutrition, Diabetes, Human Genome, Minority Health, Genetics, Cardiovascular, Health Disparities, Prevention, Precision Medicine, Clinical Research, Metabolic and endocrine, Good Health and Well Being, Humans, Glycated Hemoglobin, Diet, Diabetes Mellitus, Eating, Guanine Nucleotide Dissociation Inhibitors, Genome-Wide Association Study, Medical and Health Sciences, Endocrinology & Metabolism, Biomedical and clinical sciences
Abstract

Few studies have demonstrated reproducible gene-diet interactions (GDIs) impacting metabolic disease risk factors, likely due in part to measurement error in dietary intake estimation and insufficient capture of rare genetic variation. We aimed to identify GDIs across the genetic frequency spectrum impacting the macronutrient-glycemia relationship in genetically and culturally diverse cohorts. We analyzed 33,187 participants free of diabetes from 10 National Heart, Lung, and Blood Institute Trans-Omics for Precision Medicine program cohorts with whole-genome sequencing, self-reported diet, and glycemic trait data. We fit cohort-specific, multivariable-adjusted linear mixed models for the effect of diet, modeled as an isocaloric substitution of carbohydrate for fat, and its interactions with common and rare variants genome-wide. In main effect meta-analyses, participants consuming more carbohydrate had modestly lower glycemic trait values (e.g., for glycated hemoglobin [HbA1c], -0.013% HbA1c/250 kcal substitution). In GDI meta-analyses, a common African ancestry-enriched variant (rs79762542) reached study-wide significance and replicated in the UK Biobank cohort, indicating a negative carbohydrate-HbA1c association among major allele homozygotes only. Simulations revealed that >150,000 samples may be necessary to identify similar macronutrient GDIs under realistic assumptions about effect size and measurement error. These results generate hypotheses for further exploration of modifiable metabolic disease risk in additional cohorts with African ancestry.Article highlightsWe aimed to identify genetic modifiers of the dietary macronutrient-glycemia relationship using whole-genome sequence data from 10 Trans-Omics for Precision Medicine program cohorts. Substitution models indicated a modest reduction in glycemia associated with an increase in dietary carbohydrate at the expense of fat. Genome-wide interaction analysis identified one African ancestry-enriched variant near the FRAS1 gene that may interact with macronutrient intake to influence hemoglobin A1c. Simulation-based power calculations accounting for measurement error suggested that substantially larger sample sizes may be necessary to discover further gene-macronutrient interactions.

Published
2023

3. Host and gut microbial tryptophan metabolism and type 2 diabetes: an integrative analysis of host genetics, diet, gut microbiome and circulating metabolites in cohort studies

Author

Qi, Qibin, Li, Jun, Yu, Bing, Moon, Jee-Young, Chai, Jin C, Merino, Jordi, Hu, Jie, Ruiz-Canela, Miguel, Rebholz, Casey, Wang, Zheng, Usyk, Mykhaylo, Chen, Guo-Chong, Porneala, Bianca C, Wang, Wenshuang, Nguyen, Ngoc Quynh, Feofanova, Elena V, Grove, Megan L, Wang, Thomas J, Gerszten, Robert E, Dupuis, Josée, Salas-Salvadó, Jordi, Bao, Wei, Perkins, David L, Daviglus, Martha L, Thyagarajan, Bharat, Cai, Jianwen, Wang, Tao, Manson, JoAnn E, Martínez-González, Miguel A, Selvin, Elizabeth, Rexrode, Kathryn M, Clish, Clary B, Hu, Frank B, Meigs, James B, Knight, Rob, Burk, Robert D, Boerwinkle, Eric, and Kaplan, Robert C

Subjects
Obesity, Human Genome, Prevention, Genetics, Nutrition, Diabetes, 2.2 Factors relating to the physical environment, Aetiology, Metabolic and endocrine, Oral and gastrointestinal, Bacteria, Cohort Studies, Diabetes Mellitus, Type 2, Diet, Gastrointestinal Microbiome, Humans, Kynurenine, Lactase, Tryptophan, diabetes mellitus, dietary factors, genetics, Clinical Sciences, Paediatrics and Reproductive Medicine, Gastroenterology & Hepatology
Abstract

ObjectiveTryptophan can be catabolised to various metabolites through host kynurenine and microbial indole pathways. We aimed to examine relationships of host and microbial tryptophan metabolites with incident type 2 diabetes (T2D), host genetics, diet and gut microbiota.MethodWe analysed associations between circulating levels of 11 tryptophan metabolites and incident T2D in 9180 participants of diverse racial/ethnic backgrounds from five cohorts. We examined host genome-wide variants, dietary intake and gut microbiome associated with these metabolites.ResultsTryptophan, four kynurenine-pathway metabolites (kynurenine, kynurenate, xanthurenate and quinolinate) and indolelactate were positively associated with T2D risk, while indolepropionate was inversely associated with T2D risk. We identified multiple host genetic variants, dietary factors, gut bacteria and their potential interplay associated with these T2D-relaetd metabolites. Intakes of fibre-rich foods, but not protein/tryptophan-rich foods, were the dietary factors most strongly associated with tryptophan metabolites. The fibre-indolepropionate association was partially explained by indolepropionate-associated gut bacteria, mostly fibre-using Firmicutes. We identified a novel association between a host functional LCT variant (determining lactase persistence) and serum indolepropionate, which might be related to a host gene-diet interaction on gut Bifidobacterium, a probiotic bacterium significantly associated with indolepropionate independent of other fibre-related bacteria. Higher milk intake was associated with higher levels of gut Bifidobacterium and serum indolepropionate only among genetically lactase non-persistent individuals.ConclusionHigher milk intake among lactase non-persistent individuals, and higher fibre intake were associated with a favourable profile of circulating tryptophan metabolites for T2D, potentially through the host-microbial cross-talk shifting tryptophan metabolism toward gut microbial indolepropionate production.

Published
2022

4. Impact of individual and environmental factors on dietary or lifestyle interventions to prevent type 2 diabetes development: a systematic review

Author

Bodhini, Dhanasekaran, Morton, Robert W., Santhakumar, Vanessa, Nakabuye, Mariam, Pomares-Millan, Hugo, Clemmensen, Christoffer, Fitzpatrick, Stephanie L., Guasch-Ferre, Marta, Pankow, James S., Ried-Larsen, Mathias, Franks, Paul W., Tobias, Deirdre K., Merino, Jordi, Mohan, Viswanathan, and Loos, Ruth J. F.

Published
2023
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5. Obesity Partially Mediates the Diabetogenic Effect of Lowering LDL Cholesterol.

Author

Wu, Peitao, Moon, Jee-Young, Daghlas, Iyas, Franco, Giulianini, Porneala, Bianca, Ahmadizar, Fariba, Richardson, Tom, Isaksen, Jonas, Hindy, Georgy, Yao, Jie, Sitlani, Colleen, Raffield, Laura, Yanek, Lisa, Feitosa, Mary, Cuadrat, Rafael, Qi, Qibin, Arfan Ikram, M, Ellervik, Christina, Ericson, Ulrika, Goodarzi, Mark, Brody, Jennifer, Lange, Leslie, Mercader, Josep, Vaidya, Dhananjay, An, Ping, Schulze, Matthias, Masana, Lluis, Ghanbari, Mohsen, Olesen, Morten, Cai, Jianwen, Guo, Xiuqing, Floyd, James, Jäger, Susanne, Province, Michael, Kalyani, Rita, Psaty, Bruce, Orho-Melander, Marju, Ridker, Paul, Kanters, Jørgen, Uitterlinden, Andre, Davey Smith, George, Gill, Dipender, Kaplan, Robert, Kavousi, Maryam, Raghavan, Sridharan, Chasman, Daniel, Rotter, Jerome, Meigs, James, Florez, Jose, Dupuis, Josée, Liu, Ching-Ti, and Merino, Jordi

Subjects
Cholesterol, LDL, Diabetes Mellitus, Type 2, Genome-Wide Association Study, Humans, Mendelian Randomization Analysis, Obesity, Risk Factors
Abstract

OBJECTIVE: LDL cholesterol (LDLc)-lowering drugs modestly increase body weight and type 2 diabetes risk, but the extent to which the diabetogenic effect of lowering LDLc is mediated through increased BMI is unknown. RESEARCH DESIGN AND METHODS: We conducted summary-level univariable and multivariable Mendelian randomization (MR) analyses in 921,908 participants to investigate the effect of lowering LDLc on type 2 diabetes risk and the proportion of this effect mediated through BMI. We used data from 92,532 participants from 14 observational studies to replicate findings in individual-level MR analyses. RESULTS: A 1-SD decrease in genetically predicted LDLc was associated with increased type 2 diabetes odds (odds ratio [OR] 1.12 [95% CI 1.01, 1.24]) and BMI (β = 0.07 SD units [95% CI 0.02, 0.12]) in univariable MR analyses. The multivariable MR analysis showed evidence of an indirect effect of lowering LDLc on type 2 diabetes through BMI (OR 1.04 [95% CI 1.01, 1.08]) with a proportion mediated of 38% of the total effect (P = 0.03). Total and indirect effect estimates were similar across a number of sensitivity analyses. Individual-level MR analyses confirmed the indirect effect of lowering LDLc on type 2 diabetes through BMI with an estimated proportion mediated of 8% (P = 0.04). CONCLUSIONS: These findings suggest that the diabetogenic effect attributed to lowering LDLc is partially mediated through increased BMI. Our results could help advance understanding of adipose tissue and lipids in type 2 diabetes pathophysiology and inform strategies to reduce diabetes risk among individuals taking LDLc-lowering medications.

Published
2022

8. Sugar-Sweetened Beverage Consumption May Modify Associations Between Genetic Variants in the CHREBP (Carbohydrate Responsive Element Binding Protein) Locus and HDL-C (High-Density Lipoprotein Cholesterol) and Triglyceride Concentrations.

Author

Haslam, Danielle E, Peloso, Gina M, Guirette, Melanie, Imamura, Fumiaki, Bartz, Traci M, Pitsillides, Achilleas N, Wang, Carol A, Li-Gao, Ruifang, Westra, Jason M, Pitkänen, Niina, Young, Kristin L, Graff, Mariaelisa, Wood, Alexis C, Braun, Kim VE, Luan, Jian'an, Kähönen, Mika, Kiefte-de Jong, Jessica C, Ghanbari, Mohsen, Tintle, Nathan, Lemaitre, Rozenn N, Mook-Kanamori, Dennis O, North, Kari, Helminen, Mika, Mossavar-Rahmani, Yasmin, Snetselaar, Linda, Martin, Lisa W, Viikari, Jorma S, Oddy, Wendy H, Pennell, Craig E, Rosendall, Frits R, Ikram, M Arfan, Uitterlinden, Andre G, Psaty, Bruce M, Mozaffarian, Dariush, Rotter, Jerome I, Taylor, Kent D, Lehtimäki, Terho, Raitakari, Olli T, Livingston, Kara A, Voortman, Trudy, Forouhi, Nita G, Wareham, Nick J, de Mutsert, Renée, Rich, Steven S, Manson, JoAnn E, Mora, Samia, Ridker, Paul M, Merino, Jordi, Meigs, James B, Dashti, Hassan S, Chasman, Daniel I, Lichtenstein, Alice H, Smith, Caren E, Dupuis, Josée, Herman, Mark A, and McKeown, Nicola M

Subjects
Humans, Triglycerides, Polymorphism, Single Nucleotide, Adult, Middle Aged, Female, Male, Basic Helix-Loop-Helix Leucine Zipper Transcription Factors, Cholesterol, HDL, Meta-Analysis as Topic, Sugar-Sweetened Beverages, carbohydrates, dyslipidemia, epidemiology, genetics, nutrition, sugars, triglyceride, Cardiovascular, Genetics, Human Genome
Abstract

BackgroundChREBP (carbohydrate responsive element binding protein) is a transcription factor that responds to sugar consumption. Sugar-sweetened beverage (SSB) consumption and genetic variants in the CHREBP locus have separately been linked to HDL-C (high-density lipoprotein cholesterol) and triglyceride concentrations. We hypothesized that SSB consumption would modify the association between genetic variants in the CHREBP locus and dyslipidemia.MethodsData from 11 cohorts from the Cohorts for Heart and Aging Research in Genomic Epidemiology consortium (N=63 599) and the UK Biobank (N=59 220) were used to quantify associations of SSB consumption, genetic variants, and their interaction on HDL-C and triglyceride concentrations using linear regression models. A total of 1606 single nucleotide polymorphisms within or near CHREBP were considered. SSB consumption was estimated from validated questionnaires, and participants were grouped by their estimated intake.ResultsIn a meta-analysis, rs71556729 was significantly associated with higher HDL-C concentrations only among the highest SSB consumers (β, 2.12 [95% CI, 1.16-3.07] mg/dL per allele; P

Published
2021

10. Interaction Between Type 2 Diabetes Prevention Strategies and Genetic Determinants of Coronary Artery Disease on Cardiometabolic Risk Factors

Author

Merino, Jordi, Jablonski, Kathleen A, Mercader, Josep M, Kahn, Steven E, Chen, Ling, Harden, Maegan, Delahanty, Linda M, Araneta, Maria Rosario G, Walford, Geoffrey A, Jacobs, Suzanne BR, Ibebuogu, Uzoma N, Franks, Paul W, Knowler, William C, Florez, Jose C, Bray, George A, Gadde, Kishore, Chatellier, Annie, Arceneaux, Jennifer, Dragg, Amber, Duncan, Crystal, Greenway, Frank L, Hsia, Daniel, Levy, Erma, Lockett, Monica, Ryan, Donna H, Ehrmann, David, Matulik, Margaret J, Czech, Kirsten, DeSandre, Catherine, Goldstein, Barry J, Furlong, Kevin, Smith, Kellie A, Wildman, Wendi, Pepe, Constance, Goldberg, Ronald B, Calles, Jeanette, Ojito, Juliet, Castillo-Florez, Sumaya, Florez, Hermes J, Giannella, Anna, Lara, Olga, Veciana, Beth, Haffner, Steven M, Hazuda, Helen P, Montez, Maria G, Hattaway, Kathy, Lorenzo, Carlos, Martinez, Arlene, Walker, Tatiana, Hamman, Richard F, Dabelea, Dana, Testaverde, Lisa, Anderson, Denise, Bouffard, Alexis, Jenkins, Tonya, Lenz, Dione, Perreault, Leigh, Price, David W, Steinke, Sheila C, Horton, Edward S, Poirier, Catherine S, Swift, Kati, Caballero, Enrique, Fargnoli, Barbara, Guidi, Ashley, Guido, Mathew, Jackson, Sharon D, Lambert, Lori, Lawton, Kathleen E, Ledbury, Sarah, Sansoucy, Jessica, Spellman, Jeanne, Montgomery, Brenda K, Fujimoto, Wilfred, Knopp, Robert H, Lipkin, Edward W, Morgan-Taggart, Ivy, Murillo, Anne, Taylor, Lonnese, Thomas, April, Tsai, Elaine C, Trence, Dace, Kitabchi, Abbas E, Dagogo-Jack, Samuel, Murphy, Mary E, Taylor, Laura, Dolgoff, Jennifer, Clark, Debra, Ibebuogu, Uzoma, Lambeth, Helen, Ricks, Harriet, Rutledge, Lily MK, Soberman, Judith E, Molitch, Mark E, Metzger, Boyd E, Johnson, Mariana K, Giles, Mimi M, Larsen, Diane, and Pen, Samsam C

Subjects
Biomedical and Clinical Sciences, Diabetes, Clinical Research, Atherosclerosis, Clinical Trials and Supportive Activities, Cardiovascular, Heart Disease, Prevention, Heart Disease - Coronary Heart Disease, Nutrition, Obesity, Metabolic and endocrine, Good Health and Well Being, Adult, Cardiovascular Diseases, Coronary Artery Disease, Diabetes Mellitus, Type 2, Exercise, Exercise Therapy, Female, Gene-Environment Interaction, Genetic Predisposition to Disease, Humans, Life Style, Male, Metabolic Syndrome, Metformin, Middle Aged, Prediabetic State, Preventive Health Services, Risk Factors, United States, Diabetes Prevention Program Research Group, Medical and Health Sciences, Endocrinology & Metabolism, Biomedical and clinical sciences
Abstract

Coronary artery disease (CAD) is more frequent among individuals with dysglycemia. Preventive interventions for diabetes can improve cardiometabolic risk factors (CRFs), but it is unclear whether the benefits on CRFs are similar for individuals at different genetic risk for CAD. We built a 201-variant polygenic risk score (PRS) for CAD and tested for interaction with diabetes prevention strategies on 1-year changes in CRFs in 2,658 Diabetes Prevention Program (DPP) participants. We also examined whether separate lifestyle behaviors interact with PRS and affect changes in CRFs in each intervention group. Participants in both the lifestyle and metformin interventions had greater improvement in the majority of recognized CRFs compared with placebo (P < 0.001) irrespective of CAD genetic risk (P interaction > 0.05). We detected nominal significant interactions between PRS and dietary quality and physical activity on 1-year change in BMI, fasting glucose, triglycerides, and HDL cholesterol in individuals randomized to metformin or placebo, but none of them achieved the multiple-testing correction for significance. This study confirms that diabetes preventive interventions improve CRFs regardless of CAD genetic risk and delivers hypothesis-generating data on the varying benefit of increasing physical activity and improving diet on intermediate cardiovascular risk factors depending on individual CAD genetic risk profile.

Published
2020

11. Genome-wide meta-analysis of macronutrient intake of 91,114 European ancestry participants from the cohorts for heart and aging research in genomic epidemiology consortium

Author

Merino, Jordi, Dashti, Hassan S, Li, Sherly X, Sarnowski, Chloé, Justice, Anne E, Graff, Misa, Papoutsakis, Constantina, Smith, Caren E, Dedoussis, George V, Lemaitre, Rozenn N, Wojczynski, Mary K, Männistö, Satu, Ngwa, Julius S, Kho, Minjung, Ahluwalia, Tarunveer S, Pervjakova, Natalia, Houston, Denise K, Bouchard, Claude, Huang, Tao, Orho-Melander, Marju, Frazier-Wood, Alexis C, Mook-Kanamori, Dennis O, Pérusse, Louis, Pennell, Craig E, de Vries, Paul S, Voortman, Trudy, Li, Olivia, Kanoni, Stavroula, Rose, Lynda M, Lehtimäki, Terho, Zhao, Jing Hua, Feitosa, Mary F, Luan, Jian’an, McKeown, Nicola M, Smith, Jennifer A, Hansen, Torben, Eklund, Niina, Nalls, Mike A, Rankinen, Tuomo, Huang, Jinyan, Hernandez, Dena G, Schulz, Christina-Alexandra, Manichaikul, Ani, Li-Gao, Ruifang, Vohl, Marie-Claude, Wang, Carol A, van Rooij, Frank JA, Shin, Jean, Kalafati, Ioanna P, Day, Felix, Ridker, Paul M, Kähönen, Mika, Siscovick, David S, Langenberg, Claudia, Zhao, Wei, Astrup, Arne, Knekt, Paul, Garcia, Melissa, Rao, DC, Qi, Qibin, Ferrucci, Luigi, Ericson, Ulrika, Blangero, John, Hofman, Albert, Pausova, Zdenka, Mikkilä, Vera, Wareham, Nick J, Kardia, Sharon LR, Pedersen, Oluf, Jula, Antti, Curran, Joanne E, Zillikens, M Carola, Viikari, Jorma S, Forouhi, Nita G, Ordovás, José M, Lieske, John C, Rissanen, Harri, Uitterlinden, André G, Raitakari, Olli T, Kiefte-de Jong, Jessica C, Dupuis, Josée, Rotter, Jerome I, North, Kari E, Scott, Robert A, Province, Michael A, Perola, Markus, Cupples, L Adrienne, Turner, Stephen T, Sørensen, Thorkild IA, Salomaa, Veikko, Liu, Yongmei, Sung, Yun J, Qi, Lu, Bandinelli, Stefania, Rich, Stephen S, de Mutsert, Renée, Tremblay, Angelo, Oddy, Wendy H, Franco, Oscar H, and Paus, Tomas

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Obesity, Human Genome, Genetics, Prevention, Clinical Research, 2.1 Biological and endogenous factors, Aetiology, Cardiovascular, Metabolic and endocrine, Aged, Aging, Alpha-Ketoglutarate-Dependent Dioxygenase FTO, Cohort Studies, Energy Intake, Female, Fibroblast Growth Factors, Genetic Loci, Genetic Predisposition to Disease, Genome-Wide Association Study, Genomics, Genotype, Heart Diseases, Humans, Male, Membrane Proteins, Middle Aged, Nutrients, Polymorphism, Single Nucleotide, Receptors, Retinoic Acid, White People, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry, Clinical sciences, Biological psychology, Clinical and health psychology
Abstract

Macronutrient intake, the proportion of calories consumed from carbohydrate, fat, and protein, is an important risk factor for metabolic diseases with significant familial aggregation. Previous studies have identified two genetic loci for macronutrient intake, but incomplete coverage of genetic variation and modest sample sizes have hindered the discovery of additional loci. Here, we expanded the genetic landscape of macronutrient intake, identifying 12 suggestively significant loci (P

Published
2019

12. Time-to-Event Genome-Wide Association Study for Incident Cardiovascular Disease in People with Type 2 Diabetes Mellitus

Author

Heon Kwak, Soo, primary, B. Hernandez-Cancela, Ryan, primary, A DiCorpo, Daniel, primary, E. Condon, David, primary, Merino, Jordi, primary, Wu, Peitao, primary, A Brody, Jennifer, primary, Yao, Jie, primary, Guo, Xiuqing, primary, Ahmadizar, Fariba, primary, Meyer, Mariah, primary, Sincan, Murat, primary, M. Mercader, Josep, primary, Lee, Sujin, primary, Haessler, Jeffrey, primary, My T. Vy, Ha, primary, Lin, Zhaotong, primary, D. Armstrong, Nicole, primary, Gu, Shaopeng, primary, L. Tsao, Noah, primary, A. Lange, Leslie, primary, Wang, Ningyuan, primary, L. Wiggins, Kerri, primary, Trompet, Stella, primary, Liu, Simin, primary, J.F. Loos, Ruth, primary, Judy, Renae, primary, H. Schroeder, Philip, primary, Hasbani, Natalie R., primary, M. Bos, Maxime, primary, C. Morrison, Alanna, primary, D. Jackson, Rebecca, primary, P. Reiner, Alexander, primary, E. Manson, JoAnn, primary, S. Chaudhary, Ninad, primary, K. Carmichael, Lynn, primary, Ida Chen, Yii-Der, primary, D. Taylor, Kent, primary, Ghanbari, Mohsen, primary, van Meurs, Joyce, primary, N Pitsillides, Achilleas, primary, M. Psaty, Bruce, primary, Noordam, Raymond, primary, Do, Ron, primary, Soo Park, Kyong, primary, Wouter Jukema, J, primary, Kavousi, Maryam, primary, Correa, Adolfo, primary, S. Rich, Stephen, primary, M. Damrauer, Scott, primary, Hajek, Catherine, primary, H. Cho, Nam, primary, R. Irvin, Marguerite, primary, S. Pankow, James, primary, N. Nadkarni, Girish, primary, Sladek, Robert, primary, O. Goodarzi, Mark, primary, C. Florez, Jose, primary, I. Chasman, Daniel, primary, R. Heckbert, Susan, primary, Kooperberg, Charles, primary, Dupuis, Josée, primary, Malhotra, Rajeev, primary, S. de Vries, Paul, primary, Liu, Ching-Ti, primary, I. Rotter, Jerome, primary, and B. Meigs, James, primary

Published
2024
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14. Author Correction: Self-reported COVID-19 vaccine hesitancy and uptake among participants from different racial and ethnic groups in the United States and United Kingdom

Author

Nguyen, Long H., Joshi, Amit D., Drew, David A., Merino, Jordi, Ma, Wenjie, Lo, Chun-Han, Kwon, Sohee, Wang, Kai, Graham, Mark S., Polidori, Lorenzo, Menni, Cristina, Sudre, Carole H., Anyane-Yeboa, Adjoa, Astley, Christina M., Warner, Erica T., Hu, Christina Y., Selvachandran, Somesh, Davies, Richard, Nash, Denis, Franks, Paul W., Wolf, Jonathan, Ourselin, Sebastien, Steves, Claire J., Spector, Tim D., and Chan, Andrew T.

Published
2022
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15. Self-reported COVID-19 vaccine hesitancy and uptake among participants from different racial and ethnic groups in the United States and United Kingdom

Author

Nguyen, Long H., Joshi, Amit D., Drew, David A., Merino, Jordi, Ma, Wenjie, Lo, Chun-Han, Kwon, Sohee, Wang, Kai, Graham, Mark S., Polidori, Lorenzo, Menni, Cristina, Sudre, Carole H., Anyane-Yeboa, Adjoa, Astley, Christina M., Warner, Erica T., Hu, Christina Y., Selvachandran, Somesh, Davies, Richard, Nash, Denis, Franks, Paul W., Wolf, Jonathan, Ourselin, Sebastien, Steves, Claire J., Spector, Tim D., and Chan, Andrew T.

Published
2022
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16. Risk factors and disease profile of post-vaccination SARS-CoV-2 infection in UK users of the COVID Symptom Study app: a prospective, community-based, nested, case-control study

Author

Antonelli, Michela, Penfold, Rose S, Merino, Jordi, Sudre, Carole H, Molteni, Erika, Berry, Sarah, Canas, Liane S, Graham, Mark S, Klaser, Kerstin, Modat, Marc, Murray, Benjamin, Kerfoot, Eric, Chen, Liyuan, Deng, Jie, Österdahl, Marc F, Cheetham, Nathan J, Drew, David A, Nguyen, Long H, Pujol, Joan Capdevila, Hu, Christina, Selvachandran, Somesh, Polidori, Lorenzo, May, Anna, Wolf, Jonathan, Chan, Andrew T, Hammers, Alexander, Duncan, Emma L, Spector, Tim D, Ourselin, Sebastien, and Steves, Claire J

Published
2022
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17. Increased Genetic Risk for β-Cell Failure Is Associated With β-Cell Function Decline in People With Prediabetes.

Author

Billings, Liana K., Jablonski, Kathleen A., Pan, Qing, Florez, Jose C., Franks, Paul W., Goldberg, Ronald B., Hivert, Marie-France, Kahn, Steven E., Knowler, William C., Lee, Christine G., Merino, Jordi, Huerta-Chagoya, Alicia, Mercader, Josep M., Raghavan, Sridharan, Shi, Zhuqing, Srinivasan, Shylaja, Xu, Jianfeng, and Udler, Miriam S.

Subjects
TYPE 2 diabetes, PREDIABETIC state
Abstract

Partitioned polygenic scores (pPS) have been developed to capture pathophysiologic processes underlying type 2 diabetes (T2D). We investigated the association of T2D pPS with diabetes-related traits and T2D incidence in the Diabetes Prevention Program. We generated five T2D pPS (β-cell, proinsulin, liver/lipid, obesity, lipodystrophy) in 2,647 participants randomized to intensive lifestyle, metformin, or placebo arms. Associations were tested with general linear models and Cox regression with adjustment for age, sex, and principal components. Sensitivity analyses included adjustment for BMI. Higher β-cell pPS was associated with lower insulinogenic index and corrected insulin response at 1-year follow-up with adjustment for baseline measures (effect per pPS SD −0.04, P = 9.6 × 10−7, and −8.45 μU/mg, P = 5.6 × 10−6, respectively) and with increased diabetes incidence with adjustment for BMI at nominal significance (hazard ratio 1.10 per SD, P = 0.035). The liver/lipid pPS was associated with reduced 1-year baseline-adjusted triglyceride levels (effect per SD −4.37, P = 0.001). There was no significant interaction between T2D pPS and randomized groups. The remaining pPS were associated with baseline measures only. We conclude that despite interventions for diabetes prevention, participants with a high genetic burden of the β-cell cluster pPS had worsening in measures of β-cell function. Article Highlights: Process-specific genetic scores (partitioned polygenic scores [pPS]) help explain clinical heterogeneity in type 2 diabetes, but whether they inform differential response to diabetes prevention strategies is unknown. In the Diabetes Prevention Program clinical trial, we examined pPS for associations with longitudinal glycemic measures and type 2 diabetes development. Higher β-cell pPS was associated with reduced baseline and even further reduced 1-year β-cell function despite diabetes prevention interventions. Diabetes prevention interventions may not optimally prevent β-cell function decline in people with genetic susceptibility for β-cell dysfunction, which may contribute to the heterogeneity seen in progression of diabetes development. [ABSTRACT FROM AUTHOR]

Published
2024
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18. Diet and lifestyle behaviour disruption related to the pandemic was varied and bidirectional among US and UK adults participating in the ZOE COVID Study

Author

Mazidi, Mohsen, Leeming, Emily R., Merino, Jordi, Nguyen, Long H., Selvachandran, Somesh, Pujal, Joan Capdavila, Maher, Tyler, Kadé, Kirstin, Murray, Benjamin, Graham, Mark S., Sudre, Carole H., Wolf, Jonathan, Hu, Christina, Drew, David A., Steves, Claire J., Ourselin, Sebastien, Gardner, Christopher, Spector, Tim D., Chan, Andrew T., Franks, Paul W., Gibson, Rachel, and Berry, Sarah E.

Published
2021
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19. Vaccine side-effects and SARS-CoV-2 infection after vaccination in users of the COVID Symptom Study app in the UK: a prospective observational study

Author

Menni, Cristina, Klaser, Kerstin, May, Anna, Polidori, Lorenzo, Capdevila, Joan, Louca, Panayiotis, Sudre, Carole H, Nguyen, Long H, Drew, David A, Merino, Jordi, Hu, Christina, Selvachandran, Somesh, Antonelli, Michela, Murray, Benjamin, Canas, Liane S, Molteni, Erika, Graham, Mark S, Modat, Marc, Joshi, Amit D, Mangino, Massimo, Hammers, Alexander, Goodman, Anna L, Chan, Andrew T, Wolf, Jonathan, Steves, Claire J, Valdes, Ana M, Ourselin, Sebastien, and Spector, Tim D

Published
2021
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20. Reporting guidelines for precision medicine research of clinical relevance: the BePRECISE checklist

Author

Lim, Siew S., Semnani-Azad, Zhila, Morieri, Mario L., Ng, Ashley H., Ahmad, Abrar, Fitipaldi, Hugo, Boyle, Jacqueline, Collin, Christian, Dennis, John M., Langenberg, Claudia, Loos, Ruth J. F., Morrison, Melinda, Ramsay, Michele, Sanyal, Arun J., Sattar, Naveed, Hivert, Marie-France, Gomez, Maria F., Merino, Jordi, Tobias, Deirdre K., Trenell, Michael I., Rich, Stephen S., Sargent, Jennifer L., and Franks, Paul W.

Abstract

Precision medicine should aspire to reduce error and improve accuracy in medical and health recommendations by comparison with contemporary practice, while maintaining safety and cost-effectiveness. The etiology, clinical manifestation and prognosis of diseases such as obesity, diabetes, cardiovascular disease, kidney disease and fatty liver disease are heterogeneous. Without standardized reporting, this heterogeneity, combined with the diversity of research tools used in precision medicine studies, makes comparisons across studies and implementation of the findings challenging. Specific recommendations for reporting precision medicine research do not currently exist. The BePRECISE (Better Precision-data Reporting of Evidence from Clinical Intervention Studies & Epidemiology) consortium, comprising 23 experts in precision medicine, cardiometabolic diseases, statistics, editorial and lived experience, conducted a scoping review and participated in a modified Delphi and nominal group technique process to develop guidelines for reporting precision medicine research. The BePRECISE checklist comprises 23 items organized into 5 sections that align with typical sections of a scientific publication. A specific section about health equity serves to encourage precision medicine research to be inclusive of individuals and communities that are traditionally under-represented in clinical research and/or underserved by health systems. Adoption of BePRECISE by investigators, reviewers and editors will facilitate and accelerate equitable clinical implementation of precision medicine.

Published
2024
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21. Attributes and predictors of long COVID

Author

Sudre, Carole H., Murray, Benjamin, Varsavsky, Thomas, Graham, Mark S., Penfold, Rose S., Bowyer, Ruth C., Pujol, Joan Capdevila, Klaser, Kerstin, Antonelli, Michela, Canas, Liane S., Molteni, Erika, Modat, Marc, Jorge Cardoso, M., May, Anna, Ganesh, Sajaysurya, Davies, Richard, Nguyen, Long H., Drew, David A., Astley, Christina M., Joshi, Amit D., Merino, Jordi, Tsereteli, Neli, Fall, Tove, Gomez, Maria F., Duncan, Emma L., Menni, Cristina, Williams, Frances M. K., Franks, Paul W., Chan, Andrew T., Wolf, Jonathan, Ourselin, Sebastien, Spector, Tim, and Steves, Claire J.

Published
2021
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22. Time-to-Event Genome-Wide Association Study for Incident Cardiovascular Disease in People With Type 2 Diabetes.

Author

Kwak, Soo Heon, Hernandez-Cancela, Ryan B., DiCorpo, Daniel A., Condon, David E., Merino, Jordi, Wu, Peitao, Brody, Jennifer A., Yao, Jie, Guo, Xiuqing, Ahmadizar, Fariba, Meyer, Mariah, Sincan, Murat, Mercader, Josep M., Lee, Sujin, Haessler, Jeffrey, Vy, Ha My T., Lin, Zhaotong, Armstrong, Nicole D., Gu, Shaopeng, and Tsao, Noah L.

Subjects
GENOME-wide association studies, TYPE 2 diabetes, CARDIOVASCULAR diseases, CARDIOVASCULAR diseases risk factors, CORONARY artery disease
Abstract

OBJECTIVE: To identify genetic risk factors for incident cardiovascular disease (CVD) among people with type 2 diabetes (T2D). RESEARCH DESIGN AND METHODS: We conducted a multiancestry time-to-event genome-wide association study for incident CVD among people with T2D. We also tested 204 known coronary artery disease (CAD) variants for association with incident CVD. RESULTS: Among 49,230 participants with T2D, 8,956 had incident CVD events (event rate 18.2%). We identified three novel genetic loci for incident CVD: rs147138607 (near CACNA1E/ZNF648 , hazard ratio [HR] 1.23, P = 3.6 × 10−9), rs77142250 (near HS3ST1 , HR 1.89, P = 9.9 × 10−9), and rs335407 (near TFB1M/NOX3 , HR 1.25, P = 1.5 × 10−8). Among 204 known CAD loci, 5 were associated with incident CVD in T2D (multiple comparison–adjusted P < 0.00024, 0.05/204). A standardized polygenic score of these 204 variants was associated with incident CVD with HR 1.14 (P = 1.0 × 10−16). CONCLUSIONS: The data point to novel and known genomic regions associated with incident CVD among individuals with T2D. [ABSTRACT FROM AUTHOR]

Published
2024
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26. Association of social distancing and face mask use with risk of COVID-19

Author

Kwon, Sohee, Joshi, Amit D., Lo, Chun-Han, Drew, David A., Nguyen, Long H., Guo, Chuan-Guo, Ma, Wenjie, Mehta, Raaj S., Shebl, Fatma Mohamed, Warner, Erica T., Astley, Christina M., Merino, Jordi, Murray, Benjamin, Wolf, Jonathan, Ourselin, Sebastien, Steves, Claire J., Spector, Tim D., Hart, Jaime E., Song, Mingyang, VoPham, Trang, and Chan, Andrew T.

Published
2021
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27. Human postprandial responses to food and potential for precision nutrition

Author

Berry, Sarah E., Valdes, Ana M., Drew, David A., Asnicar, Francesco, Mazidi, Mohsen, Wolf, Jonathan, Capdevila, Joan, Hadjigeorgiou, George, Davies, Richard, Al Khatib, Haya, Bonnett, Christopher, Ganesh, Sajaysurya, Bakker, Elco, Hart, Deborah, Mangino, Massimo, Merino, Jordi, Linenberg, Inbar, Wyatt, Patrick, Ordovas, Jose M., Gardner, Christopher D., Delahanty, Linda M., Chan, Andrew T., Segata, Nicola, Franks, Paul W., and Spector, Tim D.

Published
2020
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29. Polygenic scores, diet quality, and type 2 diabetes risk: An observational study among 35,759 adults from 3 US cohorts

Author

Merino, Jordi, Guasch-Ferré, Marta, Li, Jun, Chung, Wonil, Hu, Yang, Ma, Baoshan, Li, Yanping, Kang, Jae H., Kraft, Peter, Liang, Liming, Sun, Qi, Franks, Paul W., Manson, JoAnn E., Willet, Walter C., Florez, Jose C., and Hu, Frank B.

Subjects
Life style -- Health aspects, Type 2 diabetes -- Risk factors -- Genetic aspects -- Prevention, Food habits -- Health aspects, Biological sciences
Abstract

Background Both genetic and lifestyle factors contribute to the risk of type 2 diabetes, but the extent to which there is a synergistic effect of the 2 factors is unclear. The aim of this study was to examine the joint associations of genetic risk and diet quality with incident type 2 diabetes. Methods and findings We analyzed data from 35,759 men and women in the United States participating in the Nurses' Health Study (NHS) I (1986 to 2016) and II (1991 to 2017) and the Health Professionals Follow-up Study (HPFS; 1986 to 2016) with available genetic data and who did not have diabetes, cardiovascular disease, or cancer at baseline. Genetic risk was characterized using both a global polygenic score capturing overall genetic risk and pathway-specific polygenic scores denoting distinct pathophysiological mechanisms. Diet quality was assessed using the Alternate Healthy Eating Index (AHEI). Cox models were used to calculate hazard ratios (HRs) for type 2 diabetes after adjusting for potential confounders. With over 902,386 person-years of follow-up, 4,433 participants were diagnosed with type 2 diabetes. The relative risk of type 2 diabetes was 1.29 (95% confidence interval [CI] 1.25, 1.32; P < 0.001) per standard deviation (SD) increase in global polygenic score and 1.13 (1.09, 1.17; P < 0.001) per 10-unit decrease in AHEI. Irrespective of genetic risk, low diet quality, as compared to high diet quality, was associated with approximately 30% increased risk of type 2 diabetes (P.sub.interaction = 0.69). The joint association of low diet quality and increased genetic risk was similar to the sum of the risk associated with each factor alone (P.sub.interaction = 0.30). Limitations of this study include the self-report of diet information and possible bias resulting from inclusion of highly educated participants with available genetic data. Conclusions These data provide evidence for the independent associations of genetic risk and diet quality with incident type 2 diabetes and suggest that a healthy diet is associated with lower diabetes risk across all levels of genetic risk., Author(s): Jordi Merino 1,2,3,*, Marta Guasch-Ferré 4,5, Jun Li 4,6, Wonil Chung 6,7, Yang Hu 4, Baoshan Ma 6,7, Yanping Li 4, Jae H. Kang 5, Peter Kraft 6,8,9, Liming [...]

Published
2022
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30. Heterogeneous effects on type 2 diabetes and cardiovascular outcomes of genetic variants and traits associated with fasting insulin.

Author

Manning, Alisa, primary, Sevilla-González, Magdalena, additional, Smith, Kirk, additional, Wang, Ningyuan, additional, Jensen, Aubrey, additional, Litkowski, Elizabeth, additional, Kim, Hyunkyung, additional, DiCorpo, Daniel, additional, Westerman, Kenneth, additional, Cui, Jinrui, additional, Liu, Ching-Ti, additional, Yu, Chenglong, additional, McNeil, John, additional, Lacaze, Paul, additional, Chang, Kyong-Mi, additional, Tsao, Phil, additional, Phillips, Lawrence, additional, Goodarzi, Mark, additional, Sladek, Rob, additional, Rotter, Jerome, additional, Dupuis, Josee, additional, Florez, Jose, additional, Merino, Jordi, additional, Meigs, James, additional, Zhou, Jin, additional, Raghavan, Sridharan, additional, and Udler, Miriam, additional

Published
2023
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31. Time-to-Event Genome-Wide Association Study for Incident Cardiovascular Disease in People with Type 2 Diabetes Mellitus

Author

Kwak, Soo Heon, primary, Hernandez-Cancela, Ryan B., additional, DiCorpo, Daniel A, additional, Condon, David E., additional, Merino, Jordi, additional, Wu, Peitao, additional, Brody, Jennifer A, additional, Yao, Jie, additional, Guo, Xiuqing, additional, Ahmadizar, Fariba, additional, Meyer, Mariah, additional, Sincan, Murat, additional, Mercader, Josep M., additional, Lee, Sujin, additional, Haessler, Jeffrey, additional, Vy, Ha My T., additional, Lin, Zhaotong, additional, Armstrong, Nicole D., additional, Gu, Shaopeng, additional, Tsao, Noah L., additional, Lange, Leslie A., additional, Wang, Ningyuan, additional, Wiggins, Kerri L., additional, Trompet, Stella, additional, Liu, Simin, additional, Loos, Ruth J.F., additional, Judy, Renae, additional, Schroeder, Philip H., additional, Hasbani, Natalie R., additional, Bos, Maxime M., additional, Morrison, Alanna C., additional, Jackson, Rebecca D., additional, Reiner, Alexander P., additional, Manson, JoAnn E., additional, Chaudhary, Ninad S., additional, Carmichael, Lynn K., additional, Chen, Yii-Der Ida, additional, Taylor, Kent D., additional, Ghanbari, Mohsen, additional, van Meurs, Joyce, additional, Pitsillides, Achilleas N, additional, Psaty, Bruce M., additional, Noordam, Raymond, additional, Do, Ron, additional, Park, Kyong Soo, additional, Jukema, J Wouter, additional, Kavousi, Maryam, additional, Correa, Adolfo, additional, Rich, Stephen S., additional, Damrauer, Scott M., additional, Hajek, Catherine, additional, Cho, Nam H., additional, Irvin, Marguerite R., additional, Pankow, James S., additional, Nadkarni, Girish N., additional, Sladek, Robert, additional, Goodarzi, Mark O., additional, Florez, Jose C., additional, Chasman, Daniel I., additional, Heckbert, Susan R., additional, Kooperberg, Charles, additional, Dupuis, Josée, additional, Malhotra, Rajeev, additional, de Vries, Paul S., additional, Liu, Ching-Ti, additional, Rotter, Jerome I., additional, and Meigs, James B., additional

Published
2023
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34. Role of sociodemographic, clinical, behavioral, and molecular factors in precision prevention of type 2 diabetes: a systematic review

Author

Bodhini, Dhanasekaran, Morton, Robert W., Santhakumar, Vanessa, Nakabuye, Mariam, Pomares-Millan, Hugo, Clemmensen, Christoffer, Fitzpatrick, Stephanie L., Guasch-Ferre, Marta, Pankow, James S., Ried-Larsen, Mathias, Franks, Paul W., Tobias, Deirdre K., Merino, Jordi, Mohan, Viswanathan, and Loos, Ruth J.F.

Subjects
Article
Abstract

The variability in the effectiveness of type 2 diabetes (T2D) preventive interventions highlights the potential to identify the factors that determine treatment responses and those that would benefit the most from a given intervention. We conducted a systematic review to synthesize the evidence to support whether sociodemographic, clinical, behavioral, and molecular characteristics modify the efficacy of dietary or lifestyle interventions to prevent T2D. Among the 80 publications that met our criteria for inclusion, the evidence was low to very low to attribute variability in intervention effectiveness to individual characteristics such as age, sex, BMI, race/ethnicity, socioeconomic status, baseline behavioral factors, or genetic predisposition. We found evidence, albeit low certainty, to support conclusions that those with poorer health status, particularly those with prediabetes at baseline, tend to benefit more from T2D prevention strategies compared to healthier counterparts. Our synthesis highlights the need for purposefully designed clinical trials to inform whether individual factors influence the success of T2D prevention strategies.

Published
2023

37. Investigating gene-diet interactions impacting the association between macronutrient intake and glycemic traits

Author

Westerman, Kenneth E., primary, Walker, Maura E., primary, Gaynor, Sheila M., primary, Wessel, Jennifer, primary, DiCorpo, Daniel, primary, Ma, Jiantao, primary, Alonso, Alvaro, primary, Aslibekyan, Stella, primary, Baldridge, Abigail S., primary, Bertoni, Alain G., primary, Biggs, Mary L., primary, Brody, Jennifer A., primary, Chen, Yii-Der Ida, primary, Dupuis, Josee, primary, Goodarzi, Mark O., primary, Guo, Xiuqing, primary, Hasbani, Natalie R., primary, Heath, Adam, primary, Hidalgo, Bertha, primary, Irvin, Marguerite R., primary, Johnson, W. Craig, primary, Kalyani, Rita R., primary, Lange, Leslie, primary, Lemaitre, Rozenn N., primary, Liu, Ching-Ti, primary, Liu, Simin, primary, Moon, Jee-Young, primary, Nassir, Rami, primary, Pankow, James S., primary, Pettinger, Mary, primary, Raffield, Laura, primary, Rasmussen-Torvik, Laura J., primary, Selvin, Elizabeth, primary, Senn, Mackenzie K., primary, Shadyab, Aladdin H., primary, Smith, Albert V., primary, Smith, Nicholas L., primary, Steffen, Lyn, primary, Talegakwar, Sameera, primary, Taylor, Kent D., primary, Vries, Paul S. de, primary, Wilson, James G., primary, Wood, Alexis C., primary, Yanek, Lisa R., primary, Yao, Jie, primary, Zheng, Yinan, primary, Boerwinkle, Eric, primary, Morrison, Alanna C., primary, Fornage, Miriam, primary, Russell, Tracy P., primary, Psaty, Bruce M., primary, Levy, Daniel, primary, Heard-Costa, Nancy L., primary, Ramachandran, Vasan S., primary, Mathias, Rasika A., primary, Arnett, Donna K., primary, Kaplan, Robert, primary, North, Kari E., primary, Correa, Adolfo, primary, Carson, April, primary, Rotter, Jerome I., primary, Rich, Stephen S., primary, Manson, JoAnn E., primary, Reiner, Alexander P., primary, Kooperberg, Charles, primary, Florez, Jose C., primary, Meigs, James B., primary, Merino, Jordi, primary, Tobias, Deirdre K., primary, Chen, Han, primary, and Manning, Alisa K., primary

Published
2023
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38. Association and Interaction of Genetics and Area-level Socioeconomic Factors on the Prevalence of Type 2 Diabetes and Obesity

Author

Cromer, Sara J., primary, Lakhani, Chirag M., primary, Mercader, Josep, primary, Majarian, Timothy D., primary, Schroeder, Philipp, primary, Cole, Joanne B., primary, Florez, Jose C., primary, Patel, Chirag J., primary, Manning, Alisa, primary, Burnett-Bowie, Sherri-Ann M., primary, Merino, Jordi, primary, and Udler, Miriam S., primary

Published
2023
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39. Author Correction: Attributes and predictors of long COVID

Author

Sudre, Carole H., Murray, Benjamin, Varsavsky, Thomas, Graham, Mark S., Penfold, Rose S., Bowyer, Ruth C., Pujol, Joan Capdevila, Klaser, Kerstin, Antonelli, Michela, Canas, Liane S., Molteni, Erika, Modat, Marc, Jorge Cardoso, M., May, Anna, Ganesh, Sajaysurya, Davies, Richard, Nguyen, Long H., Drew, David A., Astley, Christina M., Joshi, Amit D., Merino, Jordi, Tsereteli, Neli, Fall, Tove, Gomez, Maria F., Duncan, Emma L., Menni, Cristina, Williams, Frances M. K., Franks, Paul W., Chan, Andrew T., Wolf, Jonathan, Ourselin, Sebastien, Spector, Tim, and Steves, Claire J.

Published
2021
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40. About the Contributors

Author

Moise, Alexander R., primary, Abizaid, Alexandre, additional, Fiorelli, Alfredo Inácio, additional, de Moraes, Aline Alexandra Iannoni, additional, Sales, Allan Robson Kluser, additional, Nussbacher, Amit, additional, Andrade, Ana Cristina, additional, Lottenberg, Ana Maria, additional, Azambuja, Ana P., additional, Dantas, Ana Paula, additional, Costa, Ângela M.S., additional, Lopes, Antonio Augusto, additional, Chagas, Antonio Carlos Palandri, additional, Filho, Antonio Casela, additional, de Souza, Breno Bernardes, additional, Noel Bairey Merz, C., additional, Negrão, Carlos Eduardo, additional, Rocha Oliveira, Carlos J., additional, Echem, Cinthya, additional, Nobrega, Clara, additional, Camargo, Cristina Pires, additional, Chamie, Daniel, additional, Umpierre, Daniel, additional, Fernandes, Denise C., additional, Favarato, Desidério, additional, de Oliveira, Edilamar Menezes, additional, Rodrigues, Elaine Guadelupe, additional, Hojaij, Elaine Marques, additional, Akamine, Eliana Hiromi, additional, Bouskela, Eliete, additional, Haas, Elisa Alberton, additional, Ros, Emilio, additional, de Paula, Erich Vinicius, additional, Svensjö, Erik, additional, Jones, Erika, additional, Vanderlinde, Fabiano, additional, Mónica, Fabiola Zakia, additional, Borges, Fernanda Fatureto, additional, Consolim-Colombo, Fernanda Marciano, additional, Roque, Fernanda Roberta, additional, Bacal, Fernando, additional, Fonseca, Francisco Antonio Helfenstein, additional, Laurindo, Francisco R.M., additional, Lorenzi-Filho, Geraldo, additional, Sigal, Gilbert Alexandre, additional, De Nucci, Gilberto, additional, Garcia-Cardeña, Guillermo, additional, Oliveira de Paula, Gustavo Henrique, additional, Araújo, Haniel Alves, additional, Júnior, Heitor Moreno, additional, de Oliveira, Helena Coutinho Franco, additional, Castillo, Hozana A., additional, Monteiro, Hugo P., additional, de Campos, Iáscara Wozniak, additional, Aprahamian, Ivan, additional, Eduardo Sousa, J., additional, Ribamar Costa, J., additional, Wei, Janet, additional, Maughan, Jenna, additional, Merino, Jordi, additional, Krieger, José Eduardo, additional, Santos, José Eduardo Tanus dos, additional, de Lima, Jose Jayme Galvão, additional, Júnior, José Leudo Xavier, additional, Neto, José Rocha Faria, additional, Xavier-Neto, José, additional, Annichino-Bizzacchi, Joyce M., additional, Yugar-Toledo, Juan Carlos, additional, Tavares, Juliana Carvalho, additional, Marchini, Julio Flavio, additional, Katayama, Keyla Yukari, additional, Tanaka, Leonardo Y., additional, Colli, Lucas Giglio, additional, Drager, Luciano F., additional, Gowdak, Luís Henrique Wolff, additional, Bortolotto, Luiz Aparecido, additional, Liberman, Marcel, additional, Muscará, Marcelo Nicolás, additional, Nishiyama, Marcelo, additional, Zugaib, Marcelo, additional, Saffi, Marco Aurélio Lumertz, additional, Izar, Maria Cristina O., additional, de Carvalho, Maria Helena Catelli, additional, Alves, Maria Janieire de Nazaré Nunes, additional, Lavrador, Maria Silvia Ferrari, additional, Albuquerque, Maria Theresa O.M., additional, Clavé, Mariana Meira, additional, Moreira, Marina Beltrami, additional, Biella, Marina Maria, additional, Saad, Mario J.A., additional, Curcio, Marli F., additional, Wajngarten, Mauricio, additional, Gagliani, Mayra Luciana, additional, Andrades, Michael, additional, Afonso, Milessa Silva, additional, Carvalho, Murilo, additional, Clausell, Nadine, additional, Stolf, Noedir Antonio Groppo, additional, Berwanger, Otavio, additional, Leite, Paulo Ferreira, additional, Dourado, Paulo Magno Martins, additional, Evora, Paulo Roberto B., additional, Lemos, Pedro A., additional, Libby, Peter, additional, Shah, Prediman K., additional, da Luz, Protásio Lemos, additional, Mehta, Puja, additional, Maranhão, Raul Cavalcante, additional, Lacchini, Riccardo, additional, Kones, Richard, additional, Tostes, Rita C., additional, Borges, Roberta Eller, additional, Machado, Roberta Marcondes, additional, Giraldez, Roberto Rocha C.V., additional, Santos, Robson Augusto Souza dos, additional, Modolo, Rodrigo, additional, Gemperli, Rolf, additional, Francisco, Rossana Pulcineli Vieira, additional, Moncada, Salvador, additional, Tobar, Santiago A., additional, Kahhale, Soubhi, additional, Cau, Stefany B.A., additional, Araujo, Thaís L.S., additional, Fernandes, Tiago, additional, da Costa, Tiago Januário, additional, Costa-Hong, Valéria, additional, Esteves, Vinicius, additional, Giraldez, Viviane Zorzanelli Rocha, additional, Batista, Wagner Luiz, additional, and Sampaio, Walkyria Oliveira, additional

Published
2018
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42. Genome-wide Association Study of Change in Fasting Glucose over time in 13,807 non-diabetic European Ancestry Individuals

Author

Liu, Ching-Ti, Merino, Jordi, Rybin, Denis, DiCorpo, Daniel, Benke, Kelly S., Bragg-Gresham, Jennifer L., Canouil, Mickaël, Corre, Tanguy, Grallert, Harald, Isaacs, Aaron, Kutalik, Zoltan, Lahti, Jari, Marullo, Letizia, Marzi, Carola, Rasmussen-Torvik, Laura J., Rocheleau, Ghislain, Rueedi, Rico, Scapoli, Chiara, Verweij, Niek, Vogelzangs, Nicole, Willems, Sara M., Yengo, Loïc, Bakker, Stephan J. L., Beilby, John, Hui, Jennie, Kajantie, Eero, Müller-Nurasyid, Martina, Rathmann, Wolfgang, Balkau, Beverley, Bergmann, Sven, Eriksson, Johan G., Florez, Jose C., Froguel, Philippe, Harris, Tamara, Hung, Joseph, James, Alan L., Kavousi, Maryam, Miljkovic, Iva, Musk, Arthur W., Palmer, Lyle J., Peters, Annette, Roussel, Ronan, van der harst, Pim, van Duijn, Cornelia M., Vollenweider, Peter, Barroso, Inês, Prokopenko, Inga, Dupuis, Josée, Meigs, James B., and Bouatia-Naji, Nabila

Published
2019
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43. Using genetic variation to disentangle the complex relationship between food intake and health outcomes

Author

Pirastu, Nicola, McDonnell, Ciara, Grzeszkowiak, Eryk J, Mounier, Ninon, Imamura, Fumiaki, Merino, Jordi, Day, Felix, Zheng, Jie, Taba, Nele, Concas, Maria Pina, Repetto, Linda, Kentistou, Katherine, Robino, Antonietta, Esko, Tõnu, Joshi, Peter K, Fischer, Krista, Ong, Kenneth, Gaunt, Tom R, Kutalik, Zoltán, Perry, John RB, Wilson, James F, Pirastu, Nicola [0000-0002-5363-3886], Grzeszkowiak, Eryk J [0000-0002-6550-6366], Mounier, Ninon [0000-0001-6663-5402], Imamura, Fumiaki [0000-0002-6841-8396], Merino, Jordi [0000-0001-8312-1438], Day, Felix [0000-0003-3789-7651], Zheng, Jie [0000-0002-6623-6839], Taba, Nele [0000-0003-1953-2819], Concas, Maria Pina [0000-0003-3598-2537], Repetto, Linda [0000-0002-7901-4549], Kentistou, Katherine [0000-0002-5816-664X], Robino, Antonietta [0000-0003-2152-2353], Esko, Tõnu [0000-0003-1982-6569], Joshi, Peter K [0000-0002-6361-5059], Fischer, Krista [0000-0002-3521-0599], Ong, Kenneth [0000-0003-4689-7530], Gaunt, Tom R [0000-0003-0924-3247], Wilson, James F [0000-0001-5751-9178], Apollo - University of Cambridge Repository, Day, Felix R [0000-0003-3789-7651], Kentistou, Katherine A [0000-0002-5816-664X], and Ong, Ken K [0000-0003-4689-7530]

Subjects
Medicine and health sciences, Cancer Research, Biology and life sciences, Genetic Variation, Causality, Eating, Risk Factors, Outcome Assessment, Health Care, Genetics, Humans, Molecular Biology, Genetics (clinical), Ecology, Evolution, Behavior and Systematics, Research Article
Abstract

Diet is considered as one of the most important modifiable factors influencing human health, but efforts to identify foods or dietary patterns associated with health outcomes often suffer from biases, confounding, and reverse causation. Applying Mendelian randomization in this context may provide evidence to strengthen causality in nutrition research. To this end, we first identified 283 genetic markers associated with dietary intake in 445,779 UK Biobank participants. We then converted these associations into direct genetic effects on food exposures by adjusting them for effects mediated via other traits. The SNPs which did not show evidence of mediation were then used for MR, assessing the association between genetically predicted food choices and other risk factors, health outcomes. We show that using all associated SNPs without omitting those which show evidence of mediation, leads to biases in downstream analyses (genetic correlations, causal inference), similar to those present in observational studies. However, MR analyses using SNPs which have only a direct effect on the exposure on food exposures provided unequivocal evidence of causal associations between specific eating patterns and obesity, blood lipid status, and several other risk factors and health outcomes.

Published
2022

44. Is complying with the recommendations of sodium intake beneficial for health in individuals at high cardiovascular risk? Findings from the PREDIMED study

Author

Merino, Jordi, Guasch-Ferré, Marta, Martínez-González, Miguel A, Corella, Dolores, Estruch, Ramon, Fitó, Montserrat, Ros, Emilio, Arós, Fernando, Bulló, Mònica, Gómez-Gracia, Enrique, Moñino, Manuel, Lapetra, José, Serra-Majem, Lluís, Razquin, Cristina, Buil-Cosiales, Pilar, Sorlí, José V, Muñoz, Miguel A, Pintó, Xavier, Masana, Luis, and Salas-Salvadó, Jordi

Published
2015
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45. Changes in Population Health-Related Behaviors During a COVID-19 Surge: A Natural Experiment

Author

Delgado-Ortiz, Laura, primary, Carsin, Anne-Elie, additional, Merino, Jordi, additional, Cobo, Inés, additional, Koch, Sarah, additional, Goldberg, Ximena, additional, Chevance, Guillaume, additional, Bosch de Basea, Magda, additional, Castaño-Vinyals, Gemma, additional, Espinosa, Ana, additional, Carreras, Anna, additional, Cortes Martínez, Beatriz, additional, Straif, Kurt, additional, de Cid, Rafael, additional, Kogevinas, Manolis, additional, and Garcia-Aymerich, Judith, additional

Published
2022
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46. Publisher Correction: Human postprandial responses to food and potential for precision nutrition

Author

Berry, Sarah E., Valdes, Ana M., Drew, David A., Asnicar, Francesco, Mazidi, Mohsen, Wolf, Jonathan, Capdevila, Joan, Hadjigeorgiou, George, Davies, Richard, Khatib, Haya Al, Bonnett, Christopher, Ganesh, Sajaysurya, Bakker, Elco, Hart, Deborah, Mangino, Massimo, Merino, Jordi, Linenberg, Inbar, Wyatt, Patrick, Ordovas, Jose M., Gardner, Christopher D., Delahanty, Linda M., Chan, Andrew T., Segata, Nicola, Franks, Paul W., and Spector, Tim D.

Published
2020
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48. The mental health burden of racial and ethnic minorities during the COVID-19 pandemic

Author

Nguyen, Long H., primary, Anyane-Yeboa, Adjoa, additional, Klaser, Kerstin, additional, Merino, Jordi, additional, Drew, David A., additional, Ma, Wenjie, additional, Mehta, Raaj S., additional, Kim, Daniel Y., additional, Warner, Erica T., additional, Joshi, Amit D., additional, Graham, Mark S., additional, Sudre, Carole H., additional, Thompson, Ellen J., additional, May, Anna, additional, Hu, Christina, additional, Jørgensen, Solveig, additional, Selvachandran, Somesh, additional, Berry, Sarah E., additional, David, Sean P., additional, Martinez, Maria Elena, additional, Figueiredo, Jane C., additional, Murray, Anne M., additional, Sanders, Alan R., additional, Koenen, Karestan C., additional, Wolf, Jonathan, additional, Ourselin, Sebastien, additional, Spector, Tim D., additional, Steves, Claire J., additional, and Chan, Andrew T., additional

Published
2022
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49. Investigating gene-diet interactions impacting the association between macronutrient intake and glycemic traits

Author

Westerman, Kenneth E., primary, Walker, Maura E., additional, Gaynor, Sheila M., additional, Wessel, Jennifer, additional, DiCorpo, Daniel, additional, Ma, Jiantao, additional, Alonso, Alvaro, additional, Aslibekyan, Stella, additional, Baldridge, Abigail S., additional, Bertoni, Alain G., additional, Biggs, Mary L., additional, Brody, Jennifer A., additional, Chen, Yii-Der Ida, additional, Dupuis, Joseé, additional, Goodarzi, Mark O., additional, Guo, Xiuqing, additional, Hasbani, Natalie R., additional, Heath, Adam, additional, Hidalgo, Bertha, additional, Irvin, Marguerite R., additional, Johnson, W. Craig, additional, Kalyani, Rita R., additional, Lange, Leslie, additional, Lemaitre, Rozenn N., additional, Liu, Ching-Ti, additional, Liu, Simin, additional, Moon, Jee-Young, additional, Nassir, Rami, additional, Pankow, James S., additional, Pettinger, Mary, additional, Raffield, Laura, additional, Rasmussen-Torvik, Laura J., additional, Selvin, Elizabeth, additional, Senn, Mackenzie K., additional, Shadyab, Aladdin H., additional, Smith, Albert V., additional, Smith, Nicholas L., additional, Steffen, Lyn, additional, Talegakwar, Sameera, additional, Taylor, Kent D., additional, Vries, Paul S. de, additional, Wilson, James G., additional, Wood, Alexis C., additional, Yanek, Lisa R., additional, Yao, Jie, additional, Zheng, Yinan, additional, Boerwinkle, Eric, additional, Morrison, Alanna C., additional, Fornage, Miriam, additional, Russell, Tracy P., additional, Psaty, Bruce M., additional, Levy, Daniel, additional, Head-Costa, Nancy L., additional, Ramachandran, Vasan S., additional, Mathias, Rasika A., additional, Arnett, Donna K., additional, Kaplan, Robert, additional, North, Kari E., additional, Correa, Adolfo, additional, Carson, April, additional, Rotter, Jerome, additional, Rich, Stephen S., additional, Manson, JoAnn E., additional, Reiner, Alexander P., additional, Kooperberg, Charles, additional, Florez, Jose C., additional, Meigs, James B., additional, Merino, Jordi, additional, Tobias, Deirdre K., additional, Chen, Han, additional, and Manning, Alisa K., additional

Published
2022
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